This paper proposes a new strategy based on introducing the molecules as a replacement alternative to the well-known OMP method for a sparse representation of the time series of vegetation indices. ...This method, by preventing the occurrence of leakage problems, has led to improving the accuracy of the soft classification of agricultural products. To do so, a library of molecules (combination of atoms) is firstly generated by the representative ground truth data as the probable cultivation patterns, and then each time series of vegetation index is decomposed based on molecules. The efficiency of each molecule is measured through three different criteria 1- Maximizing the accuracy of reconstructing the input signal, 2- Minimizing the number of contributed atoms in the molecule, and 3- Minimizing the estimated negative abundances. Then, considering the uncertainties of representative atoms, an iterative imposition of constraints has been used to balance the estimated abundances. The proposed method has improved by 25.21% compared to the common OMP method in the soft classification of temporal signals of vegetation indices.
The estimation of cultivation area and categorizing the agricultural product types is one of the prerequisites for achieving sustainable development in the agricultural studies. In this study, an ...unsupervised zoning the cultivation areas with the same cultivation pattern in Golestan province is on the agenda. Therefore, due to wide spatial range, high temporal resolution and easy access of 16-day products of the vegetation of the MODIS sensor which acquired in a year (From November 2017 to October 2018), these images are used in this research. In the proposed method, after the generating of NDVI vegetation time series as a hyper-cube and separating farmlands’ boundaries in Golestan province using the land-use map; the Sequential Maximum Angle Convex Cone (SMACC) endmember extraction algorithm and the maximum number of product variation using the statistical information of the region (Obtained from the statistics centre of Iran) are used to extract endmembers of the hyper-cube. In the following, the timing responses of the NDVI, identified as endmembers, will be refined in the second phase. In this process, identifying and eliminating noise signals (unrelated to cultivating patterns) and integrating the same cultivating patterns will be on the agenda. At the last stage of the proposed method and after refinement of the endmembers, the hyper-cube is clustered by Spectral Angle Mapper (SAM) algorithm and the mapping of regions with the same cultivation pattern is produced. In the proposed method, the zoning of agricultural land is based solely on the statistical knowledge of the variety of cultivation and the results have led to the production of interconnected spatial parts. This is consistent with the reality of the spatial occurrence of similar cultivating patterns in a geographic area. On the other hand, the visual comparison of results with large scale satellite images illustrates that there is a significant relationship between clustering results and ground truth in terms of cultivating pattern. Obviously, such products can be used as initial layers of information to produce the results of a supervised classification with the aim of applying the cultivation area of a variety of agricultural products.
Mixed pixels could be considered as a major source of uncertainty through classification process of satellite imagery. In this regard, the use of soft classifiers is often inevitable in order to ...increase the accuracy of land cover estimates. Although soft classifiers provide detailed information for each pixel, spatial arrangement of sub-pixels remains unknown. Super Resolution Mapping (SRM) has opened up a new horizon to produce finer spatial resolution maps using the outputs of soft classifiers. Wide variety of SRM algorithms has been developed. In this way, spatial optimisation techniques are the most applicable ones. However, random allocation of sub-pixels and iterative procedure of optimisation are the main limitations of current methods (e.g. Hopfield Neural Network, Simulated Annealing). This research attempts to provide an optimisation approach based on the pixel swapping technique in order to simplify the concept and to reduce the iteration procedure. In this paper, a brief survey is conducted on spatial optimisation based techniques of SRM. SVM and SMACC are used cooperatively to produce fractional maps as an input of SRM algorithm. The initial allocation of the sub-pixels is performed non-randomised based on the highest amounts of attractiveness. An optimisation procedure is proposed to transfer the multiple allocated sub-pixels to the non-allocated ones. This procedure usually stops with minimal iterations and is time effective. The proposed method is tested on multispectral imagery (Landsat ETM+ and Quickbird) and has demonstrated precise results particularly in boundary pixels.
Abstract
Range‐Doppler model is a transformation that relates ground and Single‐Look Complex SAR image spaces by knowing the instantaneous position and velocity of the sensor in its trajectory. Its ...instantaneous position and velocities are determined with the help of vectorial functions, which their precisions directly influence the accuracy of the range‐Doppler model. Ephemeris data is one of the sources used for the estimation of temporal functions. Some reasons can cause uncertainty in the temporal function directly derived from the satellite navigation data. Ground control points are other alternatives for calibration of the range‐Doppler model whose visual measurements are difficult in the mountainous areas of Sentinel‐1 Single‐Look Complex images due to their resolutions and distortions. The temporal functions of the range‐Doppler model are refined through numerous control points extracted from the automatic matching between Single‐Look Complex images and Digital Elevation Model. For this purpose, the radiometric and geometric similarities of the Single‐Look Complex images and Digital Elevation Model in mountainous areas are first increased based on the initial range‐Doppler model, and then their geometric discrepancies are compensated with the least‐squares image matching method. The results indicated that the proposed method in mountainous areas could automatically compensate for up to 30 pixels errors and reach sub‐pixel accuracies.
In this paper, a straightforward mathematical model is proposed to synchronize and estimate the relative parameters of videos taken with a fixed relative orientation. The foundation of this model was ...the well-known coplanarity condition that prevails between matched points of two perspective images. Nevertheless, the synchronization problem has also been incorporated into it by making the matched points dependent on time. In this method, the required control data provides by tracking the positions of moving points in the temporal and spatial overlaps of the videos. Also, the unknown parameters are estimated through the least-squares estimation of a constrained system of linearized equations. The results of implementations on different datasets have demonstrated the efficiency of the proposed method in the temporal and relative calibration of stereo videos; as it has reached on average to the one frame accuracy in synchronization and 4.3 pixels precision in generalization of relative calibrations.
•A vision-based method which allows safe, low-cost and accurate sag measurement for power lines.•Sag inspection remotely based on a single shot taken by a smartphone.•Space resection of perspective ...images in the ill-posed condition and weak geometry of control data.•Plumb lines determination in perspective images.•Integration of smartphone sensors’ data for photogrammetric solutions.•The usage of smartphone's accelerometer data to facilitate the attitude determination of its captured images.
Measuring the sag of electrical power transmission cables is an index for sensing their internal tension and safety. In this paper, a simple photogrammetric solution is proposed to measure them which only requires an image taken by a smartphone equipped with an internal accelerometer. In this method, a modified version of the well-known co-linearity condition is proposed and the 3D locus of cables has been modeled with a parabola equation. Accelerometer measurements are used to prevent the ill-posed conditions of unknowns’ estimation and are also contributed to generating the control data for the estimation of unknowns. The distance between the supporting points of cables is the only required control information for this method. The proposed solution, while simple, significantly has reduced the risks and costs of measuring the cable sags. On average, the obtained results indicate a relative accuracy of 2.6% in the estimation of the sags of cables.
In many studies regarding the field of malaria, environmental factors have been acquired in single-time, multi-time or a short-time series using remote sensing and meteorological data. Selecting the ...best periods of the year to monitor the habitats of
larvae can be effective in better and faster control of malaria outbreaks. In this article, high-risk times for three regions in Iran, including Qaleh-Ganj, Sarbaz and Bashagard counties with a history of malaria prevalence were estimated. For this purpose, a series of environmental factors affecting the growth and survival of
were used over a seven-year period through the Google Earth Engine. The results of this study indicated two high-risk times for Qaleh-Ganj and Bashagard counties and three high-risk times for Sarbaz county over the course of a year observing an increase in the abundance of
mosquitoes. Further evaluation of the results against the entomological data available in previous studies showed that the high-risk times predicted in this study were consistent with an increase in the abundance of
mosquitoes in the study areas. The proposed method is extremely useful for temporal prediction of the increase in abundance of
mosquitoes in addition to the use of optimal data aimed at monitoring the exact location of
habitats.
Surface soil moisture (SSM) is a critical factor in monitoring climate change, soil fertility, flood, and runoff modeling. Integration of satellite earth observations and field measurements data is a ...reliable approach to estimate environmental parameters in remote sensing applications. In this paper, a method is proposed to estimate the SSM by integrating synoptic weather stations and MODIS imagery on a regional scale. The data were adopted from the Soil Climate Analysis Network of the United States (US-SCAN) stations that were routinely collected in more than 220 stations from 2012 to 2015. The proposed method is a regression model composed of spectral indices including Normalized Difference Water Index (NDWI), Visible and Shortwave infrared Drought Index (VSDI), land surface temperature (LST), and estimated surface soil temperature using ordinary Kriging (OK). This method has inspired a simple integration method that used a linear combination of the remote sensing and field LST measurements. Compared to the inspired method, the proposed method has shown a 24% improvement in SSM estimation.
Precise 3D registration of Light Detection and Ranging (LiDAR) data and High-Resolution Satellite Image (HRSI) is the prerequisite in the many remote sensing applications. An automatic registration ...process involves two main steps including the detection of corresponding entities and the estimation of a relating mathematical model. Typical matching techniques, which are generally developed to match consistent data sources, are prone to fail in case of matching between heterogeneous data sources (e.g. LiDAR and optical images). This paper proposes a three-step method to give in hand an accurate and automatic 3D registration technique between LiDAR data and the HRSI. The first step introduces a new product called Optical Consistent LiDAR Product (OCLP) which is meant to be consistent with HRSI from the radiometric point of view. The OCLP is generated using raster maps of the LiDAR heights and intensities along with information of the sun position at the acquisition time of HRSI. This new product proved to be very promising as a matching entity with HRSI. In the second step, a 3D model is estimated robustly through the matched points identified by the well-known Scale Invariant Feature Transform (SIFT) technique between the OCLP and the HRSI. The last step of the proposed method aims to strengthen this 3D model which is accomplished via iterative closest edge points matching technique. To do so, the coarse 3D model is iteratively improved based on the matching results obtained on the image edges. The proposed method was implemented on 20 different datasets containing various urban textures. The results indicate the effectiveness of the proposed method since in some cases it could achieve to sub-pixel accuracies which are the utmost expectation for a registration technique.
•A method for geometric correction of raw pushbroom images.•Geometrical measurements in rectified images would be more accurate.•Using simultaneous frame images as a reference for image ...rectification.•Applicable in most of the pushbroom cameras that acquire frame images simultaneously.•Increasing measurement accuracy without ground control data.
Video-assisted navigation is a strategy that hyperspectral camera manufacturers usually use to deal with the geometric distortions imposed on raw pushbroom images, due to perturbations. In this strategy, a simultaneous video is captured during a pushbroom raw image acquisition. This article proposes an automatic geometric correction method applicable to the sensors utilizing this strategy that does not need ground control data. This method starts with an inter-calibration procedure between the two sensors, which allows using frame-based images in the correction procedure. Then, the method uses sequential transformations among consecutive video frames to produce geometrically corrected products. The experimental results demonstrate that the proposed method's sequential nature makes it so flexible that it causes more reduction in the scenes' random geometric distortions than the common methods. Hence, the accuracy of 2D and 3D transformations relating the image and the ground spaces is increased by 66.9% compared to the raw pushbroom images.
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